{"as_of":"2026-08-19T17:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:64dbed3d64fc4e6768b7561294b53251fc576d23195b41e33739f042aea3045c","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T02:25:05.587201Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2102.12769","last_updated":"2021-02-25T10:25:57Z","snapshot_observed_at":"2026-08-16T18:43:05.518098Z","submitted_at":"2021-02-25T10:25:57Z","title":"No-Regret Reinforcement Learning with Heavy-Tailed Rewards","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.12769","snapshot_observed_at":"2026-08-01T02:25:05.587201Z","title":"No-regret reinforcement learning with heavy-tailed rewards, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25492","last_updated":"2026-07-30T02:31:33Z","snapshot_observed_at":"2026-08-16T14:25:31.030335Z","submitted_at":"2026-07-28T09:29:37Z","title":"Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-01T02:25:05.587201Z"},"links":{"cited_paper":"/paper/2102.12769","citing_paper":"/paper/2607.25492"},"observation_digest":"sha256:655dd13ee1e1e12487550e6e687075a996b962d639dd1ae8c7ff2613807515f3","observation_id":"e776aafe-70aa-4632-b800-6ca0ddee303c","resolution":{"observed_at":"2026-08-01T02:25:05.587201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2102.12769/citation-record","integrity":"/paper/2102.12769/integrity","json":"/paper/2102.12769/citation-record.json","paper":"/paper/2102.12769"},"outbound":[],"paper":{"arxiv_id":"2102.12769","last_updated":"2021-02-25T10:25:57Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T18:43:05.518098Z","submitted_at":"2021-02-25T10:25:57Z","title":"No-Regret Reinforcement Learning with Heavy-Tailed Rewards"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2102.12769."}